Overview

Dataset statistics

Number of variables13
Number of observations2972
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory302.0 KiB
Average record size in memory104.0 B

Variable types

Numeric13

Alerts

gross_revenue is highly correlated with qnt_invoices and 4 other fieldsHigh correlation
recency_days is highly correlated with qnt_invoicesHigh correlation
qnt_invoices is highly correlated with gross_revenue and 3 other fieldsHigh correlation
qnt_items is highly correlated with gross_revenue and 4 other fieldsHigh correlation
qnt_products is highly correlated with gross_revenue and 3 other fieldsHigh correlation
avg_ticket is highly correlated with qnt_returns and 1 other fieldsHigh correlation
avg_recency_days is highly correlated with frequencyHigh correlation
frequency is highly correlated with avg_recency_daysHigh correlation
avg_basket_size is highly correlated with gross_revenue and 4 other fieldsHigh correlation
avg_unique_basket_size is highly correlated with avg_basket_sizeHigh correlation
qnt_returns is highly correlated with gross_revenue and 5 other fieldsHigh correlation
avg_ticket is highly skewed (γ1 = 25.17370854) Skewed
frequency is highly skewed (γ1 = 22.48955714) Skewed
qnt_returns is highly skewed (γ1 = 21.9900238) Skewed
df_index has unique values Unique
customer_id has unique values Unique
recency_days has 89 (3.0%) zeros Zeros
qnt_returns has 1484 (49.9%) zeros Zeros

Reproduction

Analysis started2022-11-08 12:34:17.395328
Analysis finished2022-11-08 12:34:48.730274
Duration31.33 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct2972
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2319.925976
Minimum0
Maximum5724
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-11-08T09:34:48.844754image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile185.55
Q1929.75
median2121.5
Q33540.25
95-th percentile5041.9
Maximum5724
Range5724
Interquartile range (IQR)2610.5

Descriptive statistics

Standard deviation1556.821729
Coefficient of variation (CV)0.6710652602
Kurtosis-1.00981964
Mean2319.925976
Median Absolute Deviation (MAD)1272
Skewness0.3427524776
Sum6894820
Variance2423693.895
MonotonicityStrictly increasing
2022-11-08T09:34:49.022680image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
30131
 
< 0.1%
30001
 
< 0.1%
30011
 
< 0.1%
30021
 
< 0.1%
30031
 
< 0.1%
30061
 
< 0.1%
30081
 
< 0.1%
30091
 
< 0.1%
30111
 
< 0.1%
Other values (2962)2962
99.7%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
57241
< 0.1%
57051
< 0.1%
56951
< 0.1%
56891
< 0.1%
56681
< 0.1%
56641
< 0.1%
56581
< 0.1%
56471
< 0.1%
56461
< 0.1%
56361
< 0.1%

customer_id
Real number (ℝ≥0)

UNIQUE

Distinct2972
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15269.35902
Minimum12347
Maximum18287
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-11-08T09:34:49.209603image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum12347
5-th percentile12617.65
Q113798.75
median15219
Q316767.25
95-th percentile17964.45
Maximum18287
Range5940
Interquartile range (IQR)2968.5

Descriptive statistics

Standard deviation1719.023795
Coefficient of variation (CV)0.1125799579
Kurtosis-1.205173491
Mean15269.35902
Median Absolute Deviation (MAD)1487.5
Skewness0.03231443878
Sum45380535
Variance2955042.808
MonotonicityNot monotonic
2022-11-08T09:34:49.386529image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
178501
 
< 0.1%
147591
 
< 0.1%
161031
 
< 0.1%
146261
 
< 0.1%
148681
 
< 0.1%
182461
 
< 0.1%
171151
 
< 0.1%
166111
 
< 0.1%
159121
 
< 0.1%
126701
 
< 0.1%
Other values (2962)2962
99.7%
ValueCountFrequency (%)
123471
< 0.1%
123481
< 0.1%
123521
< 0.1%
123561
< 0.1%
123581
< 0.1%
123591
< 0.1%
123601
< 0.1%
123621
< 0.1%
123631
< 0.1%
123641
< 0.1%
ValueCountFrequency (%)
182871
< 0.1%
182831
< 0.1%
182821
< 0.1%
182771
< 0.1%
182761
< 0.1%
182741
< 0.1%
182731
< 0.1%
182721
< 0.1%
182701
< 0.1%
182691
< 0.1%

gross_revenue
Real number (ℝ≥0)

HIGH CORRELATION

Distinct2957
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2690.963509
Minimum6.2
Maximum279138.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-11-08T09:34:49.575451image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum6.2
5-th percentile229.8825
Q1570.49
median1083.905
Q32303.175
95-th percentile7155.426
Maximum279138.02
Range279131.82
Interquartile range (IQR)1732.685

Descriptive statistics

Standard deviation10128.87831
Coefficient of variation (CV)3.764034064
Kurtosis397.8197927
Mean2690.963509
Median Absolute Deviation (MAD)668.435
Skewness17.64673284
Sum7997543.55
Variance102594175.9
MonotonicityNot monotonic
2022-11-08T09:34:49.749379image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1078.962
 
0.1%
3312
 
0.1%
1025.442
 
0.1%
598.22
 
0.1%
731.92
 
0.1%
533.332
 
0.1%
379.652
 
0.1%
2092.322
 
0.1%
745.062
 
0.1%
889.932
 
0.1%
Other values (2947)2952
99.3%
ValueCountFrequency (%)
6.21
< 0.1%
13.31
< 0.1%
151
< 0.1%
36.561
< 0.1%
451
< 0.1%
521
< 0.1%
52.21
< 0.1%
52.21
< 0.1%
62.431
< 0.1%
68.841
< 0.1%
ValueCountFrequency (%)
279138.021
< 0.1%
259657.31
< 0.1%
194550.791
< 0.1%
140450.721
< 0.1%
124564.531
< 0.1%
117379.631
< 0.1%
91062.381
< 0.1%
72882.091
< 0.1%
66653.561
< 0.1%
65039.621
< 0.1%

recency_days
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct305
Distinct (%)10.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63.88829071
Minimum0
Maximum372
Zeros89
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-11-08T09:34:49.932302image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q111
median31
Q381
95-th percentile242
Maximum372
Range372
Interquartile range (IQR)70

Descriptive statistics

Standard deviation77.75289227
Coefficient of variation (CV)1.217013187
Kurtosis2.761781321
Mean63.88829071
Median Absolute Deviation (MAD)26
Skewness1.793794175
Sum189876
Variance6045.512256
MonotonicityNot monotonic
2022-11-08T09:34:50.110233image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
399
 
3.3%
089
 
3.0%
182
 
2.8%
282
 
2.8%
772
 
2.4%
966
 
2.2%
864
 
2.2%
460
 
2.0%
1059
 
2.0%
1558
 
2.0%
Other values (295)2241
75.4%
ValueCountFrequency (%)
089
3.0%
182
2.8%
282
2.8%
399
3.3%
460
2.0%
520
 
0.7%
638
 
1.3%
772
2.4%
864
2.2%
966
2.2%
ValueCountFrequency (%)
3723
0.1%
3714
0.1%
3681
 
< 0.1%
3661
 
< 0.1%
3655
0.2%
3631
 
< 0.1%
3592
 
0.1%
3574
0.1%
3541
 
< 0.1%
3531
 
< 0.1%

qnt_invoices
Real number (ℝ≥0)

HIGH CORRELATION

Distinct56
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.719717362
Minimum1
Maximum206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-11-08T09:34:50.302505image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile17
Maximum206
Range205
Interquartile range (IQR)4

Descriptive statistics

Standard deviation8.852726069
Coefficient of variation (CV)1.547755861
Kurtosis190.9881794
Mean5.719717362
Median Absolute Deviation (MAD)2
Skewness10.77090171
Sum16999
Variance78.37075886
MonotonicityNot monotonic
2022-11-08T09:34:50.478431image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2787
26.5%
3500
16.8%
4393
13.2%
5237
 
8.0%
1190
 
6.4%
6173
 
5.8%
7138
 
4.6%
898
 
3.3%
969
 
2.3%
1055
 
1.9%
Other values (46)332
11.2%
ValueCountFrequency (%)
1190
 
6.4%
2787
26.5%
3500
16.8%
4393
13.2%
5237
 
8.0%
6173
 
5.8%
7138
 
4.6%
898
 
3.3%
969
 
2.3%
1055
 
1.9%
ValueCountFrequency (%)
2061
< 0.1%
1991
< 0.1%
1241
< 0.1%
971
< 0.1%
912
0.1%
861
< 0.1%
721
< 0.1%
622
0.1%
601
< 0.1%
571
< 0.1%

qnt_items
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1670
Distinct (%)56.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1580.63459
Minimum1
Maximum196844
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-11-08T09:34:50.663354image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile102.55
Q1296.75
median638
Q31399
95-th percentile4402.25
Maximum196844
Range196843
Interquartile range (IQR)1102.25

Descriptive statistics

Standard deviation5701.592811
Coefficient of variation (CV)3.607154271
Kurtosis517.4081275
Mean1580.63459
Median Absolute Deviation (MAD)419
Skewness18.74961311
Sum4697646
Variance32508160.58
MonotonicityNot monotonic
2022-11-08T09:34:51.012211image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31011
 
0.4%
1509
 
0.3%
889
 
0.3%
2888
 
0.3%
2608
 
0.3%
2468
 
0.3%
848
 
0.3%
2728
 
0.3%
12007
 
0.2%
3307
 
0.2%
Other values (1660)2889
97.2%
ValueCountFrequency (%)
11
< 0.1%
22
0.1%
122
0.1%
161
< 0.1%
171
< 0.1%
181
< 0.1%
191
< 0.1%
201
< 0.1%
231
< 0.1%
251
< 0.1%
ValueCountFrequency (%)
1968441
< 0.1%
802631
< 0.1%
773731
< 0.1%
699931
< 0.1%
645491
< 0.1%
641241
< 0.1%
633121
< 0.1%
583431
< 0.1%
578851
< 0.1%
502551
< 0.1%

qnt_products
Real number (ℝ≥0)

HIGH CORRELATION

Distinct468
Distinct (%)15.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.6554509
Minimum1
Maximum7838
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-11-08T09:34:51.208129image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q129
median67
Q3135
95-th percentile382
Maximum7838
Range7837
Interquartile range (IQR)106

Descriptive statistics

Standard deviation269.7680824
Coefficient of variation (CV)2.199397422
Kurtosis355.1958
Mean122.6554509
Median Absolute Deviation (MAD)44
Skewness15.71492065
Sum364532
Variance72774.8183
MonotonicityNot monotonic
2022-11-08T09:34:51.392052image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2843
 
1.4%
2037
 
1.2%
3535
 
1.2%
2935
 
1.2%
1934
 
1.1%
1533
 
1.1%
1132
 
1.1%
2631
 
1.0%
2730
 
1.0%
2530
 
1.0%
Other values (458)2632
88.6%
ValueCountFrequency (%)
16
 
0.2%
214
0.5%
315
0.5%
417
0.6%
526
0.9%
629
1.0%
718
0.6%
819
0.6%
926
0.9%
1028
0.9%
ValueCountFrequency (%)
78381
< 0.1%
56731
< 0.1%
50951
< 0.1%
45801
< 0.1%
26981
< 0.1%
23791
< 0.1%
20601
< 0.1%
18181
< 0.1%
16731
< 0.1%
16371
< 0.1%

avg_ticket
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct2969
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.9767625
Minimum2.150588235
Maximum4453.43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-11-08T09:34:51.584972image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2.150588235
5-th percentile4.91898044
Q113.12080108
median17.96548505
Q324.88290741
95-th percentile89.807625
Maximum4453.43
Range4451.279412
Interquartile range (IQR)11.76210633

Descriptive statistics

Standard deviation119.452561
Coefficient of variation (CV)3.622325297
Kurtosis814.050302
Mean32.9767625
Median Absolute Deviation (MAD)5.974456424
Skewness25.17370854
Sum98006.93816
Variance14268.91433
MonotonicityNot monotonic
2022-11-08T09:34:51.772020image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.478333332
 
0.1%
152
 
0.1%
4.1622
 
0.1%
18.152222221
 
< 0.1%
29.784166671
 
< 0.1%
22.87926231
 
< 0.1%
20.511041671
 
< 0.1%
149.0251
 
< 0.1%
21.474358971
 
< 0.1%
12.9491
 
< 0.1%
Other values (2959)2959
99.6%
ValueCountFrequency (%)
2.1505882351
< 0.1%
2.43251
< 0.1%
2.4623711341
< 0.1%
2.5112413791
< 0.1%
2.5153333331
< 0.1%
2.651
< 0.1%
2.6569318181
< 0.1%
2.7075982531
< 0.1%
2.7606215721
< 0.1%
2.7704641911
< 0.1%
ValueCountFrequency (%)
4453.431
< 0.1%
3202.921
< 0.1%
1687.21
< 0.1%
952.98751
< 0.1%
872.131
< 0.1%
841.02144931
< 0.1%
651.16833331
< 0.1%
6401
< 0.1%
624.41
< 0.1%
615.751
< 0.1%

avg_recency_days
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1258
Distinct (%)42.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.29286573
Minimum1
Maximum366
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-11-08T09:34:51.954944image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q125.9271978
median48.225
Q385.33333333
95-th percentile200.45
Maximum366
Range365
Interquartile range (IQR)59.40613553

Descriptive statistics

Standard deviation63.48195335
Coefficient of variation (CV)0.9433682554
Kurtosis4.910702354
Mean67.29286573
Median Absolute Deviation (MAD)26.225
Skewness2.066265291
Sum199994.397
Variance4029.958401
MonotonicityNot monotonic
2022-11-08T09:34:52.144864image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1425
 
0.8%
422
 
0.7%
7021
 
0.7%
720
 
0.7%
3519
 
0.6%
4918
 
0.6%
4617
 
0.6%
1117
 
0.6%
2117
 
0.6%
616
 
0.5%
Other values (1248)2780
93.5%
ValueCountFrequency (%)
116
0.5%
1.51
 
< 0.1%
213
0.4%
2.51
 
< 0.1%
2.6013986011
 
< 0.1%
315
0.5%
3.3214285711
 
< 0.1%
3.3303571431
 
< 0.1%
3.52
 
0.1%
422
0.7%
ValueCountFrequency (%)
3661
 
< 0.1%
3651
 
< 0.1%
3631
 
< 0.1%
3621
 
< 0.1%
3572
0.1%
3561
 
< 0.1%
3552
0.1%
3521
 
< 0.1%
3512
0.1%
3503
0.1%

frequency
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct1233
Distinct (%)41.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1178686684
Minimum0.005464480874
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-11-08T09:34:52.346575image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.005464480874
5-th percentile0.008928571429
Q10.01639344262
median0.02601156069
Q30.0498097723
95-th percentile1
Maximum17
Range16.99453552
Interquartile range (IQR)0.03341632968

Descriptive statistics

Standard deviation0.4289909165
Coefficient of variation (CV)3.639567007
Kurtosis822.4442034
Mean0.1178686684
Median Absolute Deviation (MAD)0.0122500928
Skewness22.48955714
Sum350.3056823
Variance0.1840332065
MonotonicityNot monotonic
2022-11-08T09:34:52.529499image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1196
 
6.6%
0.0238095238117
 
0.6%
0.062516
 
0.5%
0.0158730158715
 
0.5%
0.0454545454514
 
0.5%
0.0192307692314
 
0.5%
0.0294117647114
 
0.5%
0.0769230769213
 
0.4%
0.0714285714313
 
0.4%
0.030303030313
 
0.4%
Other values (1223)2647
89.1%
ValueCountFrequency (%)
0.0054644808741
 
< 0.1%
0.0054794520552
0.1%
0.0054945054951
 
< 0.1%
0.0056022408963
0.1%
0.0056338028172
0.1%
0.0056818181821
 
< 0.1%
0.0056980056983
0.1%
0.0057142857144
0.1%
0.0057306590261
 
< 0.1%
0.0057471264371
 
< 0.1%
ValueCountFrequency (%)
171
 
< 0.1%
61
 
< 0.1%
41
 
< 0.1%
29
 
0.3%
1.51
 
< 0.1%
1.3333333331
 
< 0.1%
1196
6.6%
0.66666666673
 
0.1%
0.55495978551
 
< 0.1%
0.53619302951
 
< 0.1%

qnt_returns
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct213
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.84219381
Minimum0
Maximum9014
Zeros1484
Zeros (%)49.9%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-11-08T09:34:52.734414image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q39
95-th percentile100
Maximum9014
Range9014
Interquartile range (IQR)9

Descriptive statistics

Standard deviation282.6771169
Coefficient of variation (CV)8.113068839
Kurtosis596.9987163
Mean34.84219381
Median Absolute Deviation (MAD)1
Skewness21.9900238
Sum103551
Variance79906.3524
MonotonicityNot monotonic
2022-11-08T09:34:52.916339image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01484
49.9%
1164
 
5.5%
2149
 
5.0%
3105
 
3.5%
489
 
3.0%
678
 
2.6%
561
 
2.1%
1251
 
1.7%
743
 
1.4%
843
 
1.4%
Other values (203)705
23.7%
ValueCountFrequency (%)
01484
49.9%
1164
 
5.5%
2149
 
5.0%
3105
 
3.5%
489
 
3.0%
561
 
2.1%
678
 
2.6%
743
 
1.4%
843
 
1.4%
941
 
1.4%
ValueCountFrequency (%)
90141
< 0.1%
80041
< 0.1%
44271
< 0.1%
37681
< 0.1%
33321
< 0.1%
28781
< 0.1%
20221
< 0.1%
20121
< 0.1%
17761
< 0.1%
15941
< 0.1%

avg_basket_size
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1979
Distinct (%)66.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean236.23718
Minimum1
Maximum6009.333333
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-11-08T09:34:53.102262image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile44
Q1103.2875
median172.2916667
Q3281.5480769
95-th percentile599.34
Maximum6009.333333
Range6008.333333
Interquartile range (IQR)178.2605769

Descriptive statistics

Standard deviation283.7187909
Coefficient of variation (CV)1.20099127
Kurtosis102.9007053
Mean236.23718
Median Absolute Deviation (MAD)82.91666667
Skewness7.705881095
Sum702096.899
Variance80496.35234
MonotonicityNot monotonic
2022-11-08T09:34:53.291184image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10011
 
0.4%
11410
 
0.3%
829
 
0.3%
869
 
0.3%
739
 
0.3%
758
 
0.3%
1368
 
0.3%
608
 
0.3%
888
 
0.3%
1637
 
0.2%
Other values (1969)2885
97.1%
ValueCountFrequency (%)
12
0.1%
21
< 0.1%
3.3333333331
< 0.1%
5.3333333331
< 0.1%
5.6666666671
< 0.1%
6.1428571431
< 0.1%
7.51
< 0.1%
91
< 0.1%
9.51
< 0.1%
111
< 0.1%
ValueCountFrequency (%)
6009.3333331
< 0.1%
42821
< 0.1%
39061
< 0.1%
3868.651
< 0.1%
28801
< 0.1%
28011
< 0.1%
2733.9444441
< 0.1%
2518.7692311
< 0.1%
2160.3333331
< 0.1%
2082.2258061
< 0.1%

avg_unique_basket_size
Real number (ℝ≥0)

HIGH CORRELATION

Distinct906
Distinct (%)30.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.49043914
Minimum0.2
Maximum259
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-11-08T09:34:53.489102image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile2
Q17.698924731
median13.6125
Q322.14464286
95-th percentile46
Maximum259
Range258.8
Interquartile range (IQR)14.44571813

Descriptive statistics

Standard deviation15.45136768
Coefficient of variation (CV)0.883417938
Kurtosis29.35401251
Mean17.49043914
Median Absolute Deviation (MAD)6.6125
Skewness3.437673424
Sum51981.58512
Variance238.7447631
MonotonicityNot monotonic
2022-11-08T09:34:53.679024image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1342
 
1.4%
941
 
1.4%
1639
 
1.3%
839
 
1.3%
1438
 
1.3%
1738
 
1.3%
536
 
1.2%
736
 
1.2%
1136
 
1.2%
1535
 
1.2%
Other values (896)2592
87.2%
ValueCountFrequency (%)
0.21
 
< 0.1%
0.253
 
0.1%
0.33333333336
0.2%
0.41
 
< 0.1%
0.40909090911
 
< 0.1%
0.512
0.4%
0.54545454551
 
< 0.1%
0.55555555561
 
< 0.1%
0.57142857141
 
< 0.1%
0.61764705881
 
< 0.1%
ValueCountFrequency (%)
2591
< 0.1%
1771
< 0.1%
1481
< 0.1%
1271
< 0.1%
1051
< 0.1%
1041
< 0.1%
1011
< 0.1%
981
< 0.1%
95.51
< 0.1%
94.333333331
< 0.1%

Interactions

2022-11-08T09:34:45.970862image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:20.719947image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:22.940027image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:24.907211image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:27.037327image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:29.066485image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:31.142624image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:33.234755image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:35.473827image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:37.395030image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:39.741058image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:41.849182image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:43.943219image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:46.119800image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:20.885879image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:23.088965image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:25.054149image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:27.189264image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:29.210426image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:31.301558image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:33.392690image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:35.622766image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:37.547966image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:39.929978image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:41.994122image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:44.098155image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:46.272737image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:21.209745image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:23.239902image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:25.202088image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:27.340200image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:29.351367image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:31.460491image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:33.550625image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:35.775702image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:37.736889image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:40.109904image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:42.141901image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:44.251092image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:46.426672image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:21.364680image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:23.388841image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:25.509961image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:27.496137image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:29.493308image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:31.617427image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:33.709559image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:35.918643image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:37.902820image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:40.264839image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:42.288846image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:44.405027image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:46.588608image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:21.524613image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:23.546775image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:25.665896image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:27.666066image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:29.801181image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:31.782359image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:33.873491image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:36.077576image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:38.066751image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:40.426772image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:42.439788image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:44.565962image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:46.731546image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:21.678551image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:23.687716image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:25.810836image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:27.813005image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:29.939123image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:31.938294image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:34.185361image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:36.215519image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:38.211690image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:40.578710image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:42.576162image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:44.714900image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:46.896478image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:21.850479image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:23.853648image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:25.973768image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:27.978936image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:30.094058image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:32.109223image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:34.355291image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:36.385449image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:38.547551image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:40.750638image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:42.743093image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:44.880830image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:47.057968image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:22.024408image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:24.012582image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:26.144697image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:28.144868image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:30.259990image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:32.281152image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:34.527220image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:36.541384image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:38.756465image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:40.917568image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:43.069957image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:45.045762image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:47.205907image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:22.174345image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:24.156521image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:26.287638image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:28.291806image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:30.405929image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:32.435088image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:34.679157image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:36.675329image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:38.919398image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:41.066507image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:43.203902image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:45.192701image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:47.527774image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:22.335278image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:24.311458image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:26.443573image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:28.451740image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:30.554868image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:32.601020image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:34.843089image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:36.826266image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:39.093325image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:41.226440image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:43.358837image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:45.359632image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:47.721693image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:22.493212image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:24.464394image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:26.600508image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:28.612673image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:30.709804image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:32.766950image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:35.007021image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:36.975204image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:39.257257image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:41.389373image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:43.508775image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:45.521567image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:47.863634image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:22.635153image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:24.603336image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:26.741449image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:28.763611image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:30.845747image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:32.915888image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:35.156961image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:37.109148image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:39.420190image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:41.536312image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:43.647718image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:45.670986image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:48.015571image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:22.787090image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:24.760272image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:26.892387image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:28.917547image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:30.991686image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:33.077821image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:35.317891image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:37.254088image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:39.586120image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:41.696245image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:43.798655image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T09:34:45.822922image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-11-08T09:34:53.843953image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.
2022-11-08T09:34:54.283773image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-08T09:34:54.559227image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-08T09:34:54.830113image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-08T09:34:55.092005image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-08T09:34:48.269464image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-08T09:34:48.606325image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexcustomer_idgross_revenuerecency_daysqnt_invoicesqnt_itemsqnt_productsavg_ticketavg_recency_daysfrequencyqnt_returnsavg_basket_sizeavg_unique_basket_size
00178505391.21371.034.01733.0297.018.15222235.50000017.00000040.050.9705880.617647
11130473232.5956.09.01390.0171.018.90403527.2500000.02830235.0154.44444411.666667
22125836705.382.015.05028.0232.028.90250023.1875000.04043150.0335.2000007.600000
3313748948.2595.05.0439.028.033.86607192.6666670.0179210.087.8000004.800000
4415100876.00333.03.080.03.0292.0000008.6000000.07317122.026.6666670.333333
55152914623.3025.014.02102.0102.045.32647123.2000000.04011529.0150.1428574.357143
66146885630.877.021.03621.0327.017.21978618.3000000.057221399.0172.4285717.047619
77178095411.9115.012.02057.061.088.71983635.7000000.03352041.0171.4166673.833333
881531160767.900.091.038194.02379.025.5434644.1444440.243316474.0419.7142866.230769
99160982005.6387.07.0613.067.029.93477647.6666670.0278750.087.5714294.857143

Last rows

df_indexcustomer_idgross_revenuerecency_daysqnt_invoicesqnt_itemsqnt_productsavg_ticketavg_recency_daysfrequencyqnt_returnsavg_basket_sizeavg_unique_basket_size
29625636177271060.2514.01.0645.066.016.0643946.01.0000006.0645.00000066.000000
2963564617232421.522.02.0203.036.011.70888912.00.1666670.0101.50000015.000000
2964564717468137.009.02.0116.05.027.4000004.00.4000000.058.0000002.500000
2965565813596697.045.02.0406.0166.04.1990367.00.2500000.0203.00000066.500000
29665664148931237.859.02.0799.073.016.9568492.00.6666670.0399.50000036.000000
2967566812479473.2010.01.0382.030.015.7733334.01.00000034.0382.00000030.000000
2968568914126706.137.03.0508.015.047.0753333.01.00000050.0169.3333334.666667
29695695135211092.390.03.0733.0435.02.5112414.50.3333330.0244.333333104.000000
2970570515060301.847.04.0262.0120.02.5153331.04.0000000.065.50000020.000000
2971572412558269.967.01.0196.011.024.5418186.01.000000196.0196.00000011.000000